21,126 research outputs found
Information spreading during emergencies and anomalous events
The most critical time for information to spread is in the aftermath of a
serious emergency, crisis, or disaster. Individuals affected by such situations
can now turn to an array of communication channels, from mobile phone calls and
text messages to social media posts, when alerting social ties. These channels
drastically improve the speed of information in a time-sensitive event, and
provide extant records of human dynamics during and afterward the event.
Retrospective analysis of such anomalous events provides researchers with a
class of "found experiments" that may be used to better understand social
spreading. In this chapter, we study information spreading due to a number of
emergency events, including the Boston Marathon Bombing and a plane crash at a
western European airport. We also contrast the different information which may
be gleaned by social media data compared with mobile phone data and we estimate
the rate of anomalous events in a mobile phone dataset using a proposed anomaly
detection method.Comment: 19 pages, 11 figure
A Grammatical Inference Approach to Language-Based Anomaly Detection in XML
False-positives are a problem in anomaly-based intrusion detection systems.
To counter this issue, we discuss anomaly detection for the eXtensible Markup
Language (XML) in a language-theoretic view. We argue that many XML-based
attacks target the syntactic level, i.e. the tree structure or element content,
and syntax validation of XML documents reduces the attack surface. XML offers
so-called schemas for validation, but in real world, schemas are often
unavailable, ignored or too general. In this work-in-progress paper we describe
a grammatical inference approach to learn an automaton from example XML
documents for detecting documents with anomalous syntax.
We discuss properties and expressiveness of XML to understand limits of
learnability. Our contributions are an XML Schema compatible lexical datatype
system to abstract content in XML and an algorithm to learn visibly pushdown
automata (VPA) directly from a set of examples. The proposed algorithm does not
require the tree representation of XML, so it can process large documents or
streams. The resulting deterministic VPA then allows stream validation of
documents to recognize deviations in the underlying tree structure or
datatypes.Comment: Paper accepted at First Int. Workshop on Emerging Cyberthreats and
Countermeasures ECTCM 201
Polymorphism and danger susceptibility of system call DASTONs
We have proposed a metaphor “DAnger Susceptible daTa codON� (DASTON) in data subject to processing by Danger Theory (DT) based Artificial Immune System (DAIS). The DASTONs are data chunks or data point sets that actively take part to produce “danger�; here we abstract “danger� as required outcome. To have closer look to the metaphor, this paper furthers biological abstractions for DASTON. Susceptibility of DASTON is important parameter for generating dangerous outcome. In biology, susceptibility of a host to pathogenic activities (potentially dangerous activities) is related to polymorphism. Interestingly, results of experiments conducted for system call DASTONs are in close accordance to biological theory of polymorphism and susceptibility. This shows that computational data (system calls in this case) exhibit biological properties when processed with DT point of view
AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments
This report considers the application of Articial Intelligence (AI) techniques to
the problem of misuse detection and misuse localisation within telecommunications
environments. A broad survey of techniques is provided, that covers inter alia
rule based systems, model-based systems, case based reasoning, pattern matching,
clustering and feature extraction, articial neural networks, genetic algorithms, arti
cial immune systems, agent based systems, data mining and a variety of hybrid
approaches. The report then considers the central issue of event correlation, that
is at the heart of many misuse detection and localisation systems. The notion of
being able to infer misuse by the correlation of individual temporally distributed
events within a multiple data stream environment is explored, and a range of techniques,
covering model based approaches, `programmed' AI and machine learning
paradigms. It is found that, in general, correlation is best achieved via rule based approaches,
but that these suffer from a number of drawbacks, such as the difculty of
developing and maintaining an appropriate knowledge base, and the lack of ability
to generalise from known misuses to new unseen misuses. Two distinct approaches
are evident. One attempts to encode knowledge of known misuses, typically within
rules, and use this to screen events. This approach cannot generally detect misuses
for which it has not been programmed, i.e. it is prone to issuing false negatives.
The other attempts to `learn' the features of event patterns that constitute normal
behaviour, and, by observing patterns that do not match expected behaviour, detect
when a misuse has occurred. This approach is prone to issuing false positives,
i.e. inferring misuse from innocent patterns of behaviour that the system was not
trained to recognise. Contemporary approaches are seen to favour hybridisation,
often combining detection or localisation mechanisms for both abnormal and normal
behaviour, the former to capture known cases of misuse, the latter to capture
unknown cases. In some systems, these mechanisms even work together to update
each other to increase detection rates and lower false positive rates. It is concluded
that hybridisation offers the most promising future direction, but that a rule or state
based component is likely to remain, being the most natural approach to the correlation
of complex events. The challenge, then, is to mitigate the weaknesses of
canonical programmed systems such that learning, generalisation and adaptation
are more readily facilitated
Unfolding the procedure of characterizing recorded ultra low frequency, kHZ and MHz electromagetic anomalies prior to the L'Aquila earthquake as pre-seismic ones. Part I
Ultra low frequency, kHz and MHz electromagnetic anomalies were recorded
prior to the L'Aquila catastrophic earthquake that occurred on April 6, 2009.
The main aims of this contribution are: (i) To suggest a procedure for the
designation of detected EM anomalies as seismogenic ones. We do not expect to
be possible to provide a succinct and solid definition of a pre-seismic EM
emission. Instead, we attempt, through a multidisciplinary analysis, to provide
elements of a definition. (ii) To link the detected MHz and kHz EM anomalies
with equivalent last stages of the L'Aquila earthquake preparation process.
(iii) To put forward physically meaningful arguments to support a way of
quantifying the time to global failure and the identification of distinguishing
features beyond which the evolution towards global failure becomes
irreversible. The whole effort is unfolded in two consecutive parts. We clarify
we try to specify not only whether or not a single EM anomaly is pre-seismic in
itself, but mainly whether a combination of kHz, MHz, and ULF EM anomalies can
be characterized as pre-seismic one
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